I did a study monitoring the stability of an enzyme exposed to different conditions over time. Each day for 30 days, an aliquot was taken from each storage sample and analyzed in duplicate. I wanted to use statistical analysis to show mean changes during the 30 day experiment between the different conditions for analysis.
Here are the statistical methods I was thinking to use:
- For condition, mean value and standard deviation (SD) values at each day will be calculated and plotted versus day
- Day 1 values of mean value will be compared across the 8 conditions using a one factor analysis of variance (ANOVA) to determine if the eight methods had comparable starting value.
- If the starting value was significantly different, then the change from Day 1 will be calculated and used for comparison of the conditions. A plot of means and SDs of change in value from Day 1 by method and day will be made
- Comparison of values from the 8 conditions across the 30 days will be made using a two factor ANOVA, with conditions and days as the two factors.
- If a significant interaction of condition by day was observed, then a one-factor ANOVA will be used for each day to compare the conditions. A p value ≤ 0.05 will be used as statistically significant
- To determine deterioration in the sample based on the condition, a one-factor ANOVA will be used to make comparisons among days for each condition. This will be followed by a one-tailed Dunnett’s test to compare Days 2-30 to Day 1. There will be up to 29 comparisons for each method (30 days-1). Some of the conditions may have less than 30 days due to the condition the sample is placed in making it unable to be tested. A reasonable experiment-wise error rate of p value < 0.01 will be used.